A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

Kaitong Wu, Xiangang Peng, Zilu Li, Wenbo Cui, Haoliang Yuan, Chun Sing Lai and Loi Lei Lai
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Kaitong Wu: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Xiangang Peng: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Zilu Li: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Wenbo Cui: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Haoliang Yuan: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Chun Sing Lai: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Loi Lei Lai: Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Energies, 2022, vol. 15, issue 15, 1-20

Abstract: High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.

Keywords: short-term PV power forecasting; trend feature extraction; fast correlation-based filter; bidirectional long short-term memory network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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